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Detector optimization based on artificial neural network training
- Authors: Roudnev V.A.1, Galaktionov K.A.1, Valiev F.F.1
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Affiliations:
- St. Petersburg State University
- Issue: Vol 89, No 8 (2025)
- Pages: 1326-1333
- Section: Fundamental problems and applications of physics of atomic nucleus
- URL: https://ogarev-online.ru/0367-6765/article/view/356133
- DOI: https://doi.org/10.7868/S3034646025080229
- ID: 356133
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Abstract
We apply artificial neural networks for event-wise analysis of model data for a microchannel plate detector. Based on this data, we estimate an impact parameter, and the collision point coordinates for each event. We have performed the analysis based on several Monte-Carlo collision models. Even though the quality of the existing models is not sufficient for a reliable, model-independent estimation of the collision parameters, the proposed method of parameter reconstruction allows us to estimate the optimal technical characteristics of the detector.
About the authors
V. A. Roudnev
St. Petersburg State UniversitySt. Petersburg, Russia
K. A. Galaktionov
St. Petersburg State University
Email: st067889@student.spbu.ru
St. Petersburg, Russia
F. F. Valiev
St. Petersburg State UniversitySt. Petersburg, Russia
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